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Steve Taylor

In my opinion, DataCamp has the most comprehensive catalog for preparing future data scientists for a degree.

For more than two decades, Steve has worked in a variety of fields including sales & marketing, math education, and compensation consulting. Today, Steve is pursuing his passion for statistics and programming and is using DataCamp to prepare himself to go back to school for a Master's degree in Data Science. It is never too late to learn something new!

What drew you to data science?

Applied statistics has always been interesting to me and has been a powerful tool in my career. Secondly, I've always had a love for computer science and programming, even though it was a field in which I had very little prior knowledge. I've decided now is the time in my life to become a part of not only an exciting field but a community that will be growing for decades to come. I love Data Science because it combines several things that I've always wanted to learn on a higher level: statistics, programming, and advanced topics in computer science like machine learning.

Tell us a little about your background.

One of my first jobs after college was a sales and marketing position in New York, but I knew it was a life I didn't want. I didn't want to work for a company that sold plastic displays and spend three or four hours commuting each day.

I knew I needed to develop some new skill sets, so in 1993 I bought a book on DOS to learn about the OS that supported Windows 3.1. I never considered myself a hard math/science person, but after this experience, I felt confident enough to take a beginning programming course at a local college. A few years later, I took a sales and marketing job at Linksys. However, I wanted to move from my prestigious sales and marketing position to tech support. The advice I got from friends, family, and even Linksys' CEO, Victor Tsao, was that I was "good at what I did and should stick to it." I took their advice, but it kept me from pursuing my interest in programming and other fields like statistics.

But when I met my wife, she encouraged me to pursue my passions. I became a math teacher and then I went on to join my wife's company and became a compensation consultant. Now I use Excel and statistical techniques daily.

Discovering DataCamp was a saving grace... I was looking for a platform where I could get more coding practice through hands-on exercises.

How did you discover DataCamp?

I learned about DataCamp while I was taking an online data science course through Johns Hopkins. Discovering DataCamp was a saving grace: it gave me a way to learn R programming through systematic explicit instruction. I, like many others, was looking for a platform where I could get more coding practice through hands-on exercises. DataCamp's format gives instant feedback on how well I'm performing on a given exercise. Whether I found DataCamp on my own or heard it through the grapevine, I knew that I hit pay dirt when I found it. At first, I felt that DataCamp would serve as a complement to the Johns Hopkins course, but the more I got into DataCamp, the more I stayed with the DataCamp platform — I now use it almost exclusively. In my opinion, DataCamp has the most comprehensive catalog for getting future data scientists ready for their work or a degree.

What were your goals when you started?

My goal was to find a source that would get me ready for a Master's of Science in Data Science degree. I am still learning what I need to learn from DataCamp to be ready for a Data Science degree when the time is right. DataCamp actually seems to carry a syllabus that is more venerable than a great deal of colleges that are creating Analytics degrees. I am in no rush to leave DataCamp until I've learned the necessary skills that will set me up for success at a university for when I begin my Data Science Master's degree.

Anyone can gain a solid understanding of Data Science knowing that DataCamp's curriculum is both accessible and credible.

As a former teacher, how do you feel the DataCamp platform aligns with theories of learning?

There are several theories of learning that are certainly at play here. The theory of the Zone of Proximal Development—that we can learn a lot when we are given just a little help—jibes well with the DataCamp platform. The way the exercises are set up gives just the right amount of support.

Also, the courses incrementally develop concepts through an ascending hierarchy. As I progressed through the courses, I felt myself engaging in all aspects of Bloom's Taxonomy: remembering, understanding, applying, synthesizing, and evaluating new concepts and techniques. The DataCamp platform provides a sequence that guides students through all aspects of what one should know before moving on to the next concept.

And finally, the process of learning should never be boring, but it shouldn't be frustrating either. I think DataCamp does an excellent job of balancing these two extremes.

What about DataCamp works well for you? What do you like?

I applaud the instruction; I applaud the content; I applaud the quality, time, and effort that DataCamp employees and creators have put into its program and platform. DataCamp has done a lot of good for the public. Anyone can gain a solid understanding of Data Science knowing that DataCamp's curriculum is both accessible and credible.

What has worked especially well for me is that DataCamp is setup to allow people to learn R, Python, Data Science and the application of Data Science through many different paths. For example, you could go from Intro to R to Intermediate R to R Practice to Writing Functions in R, and by the end, you'd be a confident R developer. The same can be said for Python, or data visualization, or any other Data Science topic. The instruction is very clear and structured, and I couldn't agree more with DataCamp's "Learn by Doing" mantra.

What advice do you have for someone just starting out with DataCamp?

I have two pieces of advice: first, find something that may complement or enhance your experience with DataCamp. I use several resources in addition to DataCamp, such as electronic notecards and a book or two on R and Python.

Second, spend a little time every day. I try to log on every day, even if it's only for a few minutes. I've found that distributing my learning into chunks and repeating exercises I've already done for more practice has solidified my learning for a lot of the concepts and syntax.

I love the platform so much, I would love to help newcomers to DataCamp stick with it. Hopefully by sharing my story, I can inspire others!

Update from the DataCamp team: Since publishing this story, Steve has started his MSc. in Data Science! Congrats, Steve!

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